Spin neuron for ultra low power computational hardware

Abstract

We propose a device model for neuron based on lateral spin valve (LSV) that constitutes of multiple input magnets, connected to an output magnet, using metal channels. The low-resistance, magneto-metallic neuron can operate at a small terminal voltage of ~20mV, while performing computation upon current-mode inputs. The spin-based neurons can be integrated with CMOS to realize ultra low-power data processing hardware, based on neural networks (NN), for different classes of applications like, cognitive computing, programmable Boolean/non-Boolean logic and analog and digital signal processing [1, 2]. In this work we present analog image acquisition and processing as an example. Results based on device-circuit co-simulation framework show that a spin-CMOS hybrid design, employing the proposed neuron, can achieve ~100x lower energy consumption per computation-frame, as compared to the state of art CMOS designs employing conventional analog circuits [13].